View of EFFICIENT CHANNEL EQUALIZATION TECHNIQUE FOR COMMUNICATION SYSTEM

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ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING

Peer Reviewed and Refereed Journal IMPACT FACTOR: 2.104 (INTERNATIONAL JOURNAL) UGC APPROVED NO. 48767

Vol.02, Issue 07, July 2017, Available Online: www.ajeee.co.in/index.php/AJEEE

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EFFICIENT CHANNEL EQUALIZATION TECHNIQUE FOR COMMUNICATION SYSTEM

1Sachin Sahu,

1M. Tech Scholar BTIRT

2Ritu Dubey,

2Asstt Prof BTIRT

Abstract:- Equalization, in spite of all its advantages, has a serious limitation of being not sensitive when the channel transfer function varies rapidly in time. The channel equalizer reconstructs or estimates the corrupted data sequence from a set of received symbols.

Equalizers have been adopted in telephone and mobile communication systems to improve the symbol error rates and the linear FIR filter has been used within equalization, which dates back to the time when loading coils were used to improve voice transmission in telephone networks In comb-type pilot based channel estimation, an efficient interpolation technique is necessary in order to estimate channel at data sub-carriers by using channel information at pilot sub-carriers. The image transmission over communication system using digital modulation techniques are performed and the results are obtained through a high level technical language called MATLAB was introduce for designing and implementing wireless digital communication system. The simulation results are performed, when SNR value is 10 dB. By using 64-QAM modulation technique, which carries higher data rates, this is essential for image signal transmission. Modulation techniques such as 64 QAM provide better results than the other modulation techniques such as QPSK and 16 QAM during Channel Estimation and Equalization

Keywords:- Channel Equalization; Wavelet Transform; Orthogonal Frequency Division Multiplexing; Independent Component Analysis; Multi-scale Independent Component Analysis.

1. INTRODUCTION

The growth in communication services during the past five decades has been phenomenal. Satellite and fiber optic networks provide high-speed communication services around the world. Most of the wire line communication systems are being replaced currently by fiber optic cables which provide extremely high bandwidth and make possible the transmission of a wide variety of information sources, including voice, data, and video. With the unimaginable development of Internet technologies, efficient high-speed data transmission techniques over communication channels have become a necessity of the day.

1.1 Channel Equalization

To counter the effect of multipath propagation there are several techniques available. The most widely used include frequency diversity, space diversity;

amplitude equalization and channel equalization (amplitude and delay correction).The first two of these require a bandwidth overhead. But, in general, bandwidth is costly. These signal diversity techniques were used in analogue radio and have been easily adapted to digital

systems that undergo highly selective interference.

The amplitude equalizers are designed to flatten the received spectrum to correct the spectral shape. An amplitude equalizer is often used in conjunction with frequency or space diversity, which can provide sufficient equalization for specific channels (minimum phase). However, to adequately characterize the effects of all channel types, (minimum and non-minimum phase), the channel equalizer is adopted.

The channel equalizer reconstructs or estimates the corrupted data sequence from a set of received symbols. Equalizers have been adopted in telephone and mobile communication systems to improve the symbol error rates and the linear FIR filter has been used within equalization, which dates back to the time when loading coils were used to improve voice transmission in telephone networks. The FIR approach classifies the received class sets to their desired output using a linear function of the filter inputs.

However, this does not always provide ideal separation of the input data points. It has been shown through Bayesian analysis that the ideal

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2 classification of a non-minimum phase channel should have non-linear characteristics. Equalization, in spite of all its advantages, has a serious limitation of being not sensitive when the channel transfer function varies rapidly in time.

Moreover, the transmission system transfer function and channel impulse response are unknown in time dispersive channels

2. CHANNEL ESTIMATION BASED ON INTERPOLATION TECHNIQUES:

Without going back to time domain channel frequency response for each subcarrier can be found by using interpolation techniques. In comb-type pilot based channel estimation, an efficient interpolation technique is necessary in order to estimate channel at data sub-carriers by using channel information at pilot sub-carriers. The input image signal which we used is the random data generated by randn function of the mat lab, and limit the data to its maximum value e.g. 16 (for 16QAM).

Fig.1 System for Channel Estimation 2.1 Serial To Parallel Conversion

The input serial data stream is formatted into the word size required for transmission, e.g. 2bit/word for QPSK, and shifted into a parallel format.

The data is then transmitted in parallel by assigning each data word to one carrier in the transmission.

2.2 Modulation Of Data

The data to be transmitted on each carrier is modulated into a QAM and Mary PSK format. The data on each symbol is mapped. In the simulations we used 16- QAM BPSK, QPSK, 8PSK Modulation.

2.3 Inverse Fourier Transform

After the required spectrum is worked out, an inverse Fourier transform is used to find the corresponding time waveform. The guard period is then added to the start of each symbol the binary information is first grouped and mapped ac-cording to the modulation in “signal mapper”. After inserting pilots either to all sub-carriers with a specific period or uniformly between the information data sequence, IDFT blocks used to transform the data sequence of length into time domain signal A wideband radio channel is normally frequency selective and time variant.

For an OFDM mobile communication system, the channel transfer function at different subcarriers appears unequal in both frequency and time domains.

Therefore, a dynamic estimation of the channel is necessary.

Pilot-based approaches are widely used to estimate the channel properties and correct the received signal. In this chapter we have investigated two types of pilot arrangements.

Fig.2 Magnitude and Phase of Equalizer

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3 Fig.3 Pilot Arrangement

In block-type pilot based channel estimation, OFDM channel estimation symbols are transmitted periodically, in which all sub-carriers are used as pilots.

If the channel is constant during the block, there will be no channel estimation error since the pilots are sent at all carriers. The estimation can be performed by using LSE.

2.4 Receiver

The receiver basically does the reverse operation to the transmitter. The guard period is removed. The FFT of each symbol is then taken to find the original transmitted spectrum. Each transmission carrier is then evaluated and converted back to the data word by demodulating the received symbol. The data words are then combined back to the same word size as the original data.

3. RESULT DISCUSSION

The image transmission over communication system using digital modulation techniques are performed and the results are obtained through a high level technical language called MATLAB was introduce for designing and implementing wireless digital communication system. Like many of the other wireless digital communication systems, the performance of this system is acceptable that, up to a certain level of noise from the critical channel.

Fig.4 Signal Distribution

In other words, if the noise level is raised above this critical level, the performance of the system cannot very rapidly.

The advantage of the currently designed system is that, when the channel is under a condition of high noise, the system generates a quality of image worse rather than completely lose the transmitted image.

Fig.5 Channel Equalization effect shown through input image The simulation results are performed, when SNR value is 10 dB. By using 64- QAM modulation technique, which carries higher data rates, this is essential for image signal transmission. Modulation techniques such as 64 QAM provide better results than the other modulation techniques such as QPSK and 16 QAM during Channel Estimation and Equalization

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4 4. CONCLUSION

OFDM has the capability of transmitting information at high data rate without increasing the transmitting power. The performance of the system can be improved by estimating the channel parameters effectively. From the simulations it is concluded MMSE algorithm estimates the channel much better than LS at the cost of increasing complexity. The results improve when the output of estimator is subject to DFT.

By using 64-QAM modulation technique, which carries higher data rates, this is essential for image signal transmission. Modulation techniques such as 64 QAM provide better results than the other modulation techniques such as QPSK and 16 QAM during Channel Estimation and Equalization. it is observed the QPSK data symbols results in less number of errors as compared to QAM at cost of decrease in symbol rate. A trade off has made for the performance of OFDM system among complexity, symbol rate and symbol errors

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